






"""simple docstring"""


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Optional[int]      =  (UnCLIPScheduler,)
					def        UpperCAmelCase__ (							self							:	Tuple     , **UpperCAmelCase							:	Optional[Any]   )	->       Optional[int]:
											lowerCAmelCase   :str						=			{
											    'num_train_timesteps': 1000,
											    'variance_type': 'fixed_small_log',
											    'clip_sample': True,
											    'clip_sample_range': 1.0,
											    'prediction_type': 'epsilon',
											}

											config.update(**UpperCAmelCase   )
											return config
					def        UpperCAmelCase__ (							self							:	str   )	->       List[Any]:
											for timesteps in [1, 5, 100, 1000]:
																	self.check_over_configs(num_train_timesteps=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       int:
											for variance in ["fixed_small_log", "learned_range"]:
																	self.check_over_configs(variance_type=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	str   )	->       Dict:
											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	str   )	->       Optional[Any]:
											for clip_sample_range in [1, 5, 10, 20]:
																	self.check_over_configs(clip_sample_range=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       List[str]:
											for prediction_type in ["epsilon", "sample"]:
																	self.check_over_configs(prediction_type=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       str:
											for time_step in [0, 500, 999]:
																	for prev_timestep in [None, 5, 100, 250, 500, 750]:
																							if prev_timestep is not None and prev_timestep >= time_step:
																													continue

																							self.check_over_forward(time_step=UpperCAmelCase     , prev_timestep=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       List[Any]:
											lowerCAmelCase   :List[Any]						=			self.scheduler_classes[0]
											lowerCAmelCase   :List[str]						=			self.get_scheduler_config(variance_type='fixed_small_log'   )
											lowerCAmelCase   :Optional[Any]						=			scheduler_class(**UpperCAmelCase   )

											assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0_0_0_0e-1_0   )   ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_5_4_9_6_2_5   )   ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_9_9_4_9_8_7   )   ) < 1e-5
					def        UpperCAmelCase__ (							self							:	Any   )	->       List[Any]:
											lowerCAmelCase   :Union[str, Any]						=			self.scheduler_classes[0]
											lowerCAmelCase   :Any						=			self.get_scheduler_config(variance_type='learned_range'   )
											lowerCAmelCase   :Tuple						=			scheduler_class(**UpperCAmelCase   )

											lowerCAmelCase   :Any						=			0.5

											assert scheduler._get_variance(1     , predicted_variance=UpperCAmelCase   ) - -1_0.1_7_1_2_7_9_0 < 1e-5
											assert scheduler._get_variance(487     , predicted_variance=UpperCAmelCase   ) - -5.7_9_9_8_0_5_2 < 1e-5
											assert scheduler._get_variance(999     , predicted_variance=UpperCAmelCase   ) - -0.0_0_1_0_0_1_1 < 1e-5
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[int]:
											lowerCAmelCase   :str						=			self.scheduler_classes[0]
											lowerCAmelCase   :Tuple						=			self.get_scheduler_config()
											lowerCAmelCase   :int						=			scheduler_class(**UpperCAmelCase   )

											lowerCAmelCase   :str						=			scheduler.timesteps

											lowerCAmelCase   :Optional[Any]						=			self.dummy_model()
											lowerCAmelCase   :int						=			self.dummy_sample_deter
											lowerCAmelCase   :Optional[Any]						=			torch.manual_seed(0   )

											for i, t in enumerate(UpperCAmelCase   ):
																	# 1. predict noise residual
																	lowerCAmelCase   :Optional[Any]						=			model(UpperCAmelCase     , UpperCAmelCase   )

																	# 2. predict previous mean of sample x_t-1
																	lowerCAmelCase   :Dict						=			scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , generator=UpperCAmelCase   ).prev_sample

																	lowerCAmelCase   :str						=			pred_prev_sample

											lowerCAmelCase   :str						=			torch.sum(torch.abs(UpperCAmelCase   )   )
											lowerCAmelCase   :str						=			torch.mean(torch.abs(UpperCAmelCase   )   )

											assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5   ) < 1e-2
											assert abs(result_mean.item() - 0.3_2_8_4_7_4_3   ) < 1e-3
					def        UpperCAmelCase__ (							self							:	int   )	->       Dict:
											lowerCAmelCase   :Any						=			self.scheduler_classes[0]
											lowerCAmelCase   :str						=			self.get_scheduler_config()
											lowerCAmelCase   :Union[str, Any]						=			scheduler_class(**UpperCAmelCase   )

											scheduler.set_timesteps(25   )

											lowerCAmelCase   :Optional[Any]						=			scheduler.timesteps

											lowerCAmelCase   :List[Any]						=			self.dummy_model()
											lowerCAmelCase   :int						=			self.dummy_sample_deter
											lowerCAmelCase   :Tuple						=			torch.manual_seed(0   )

											for i, t in enumerate(UpperCAmelCase   ):
																	# 1. predict noise residual
																	lowerCAmelCase   :List[str]						=			model(UpperCAmelCase     , UpperCAmelCase   )

																	if i + 1 == timesteps.shape[0]:
																							lowerCAmelCase   :Dict						=			None
																	else:
																							lowerCAmelCase   :Union[str, Any]						=			timesteps[i + 1]

																	# 2. predict previous mean of sample x_t-1
																	lowerCAmelCase   :Tuple						=			scheduler.step(
																	    UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , prev_timestep=UpperCAmelCase     , generator=UpperCAmelCase   ).prev_sample

																	lowerCAmelCase   :Tuple						=			pred_prev_sample

											lowerCAmelCase   :List[str]						=			torch.sum(torch.abs(UpperCAmelCase   )   )
											lowerCAmelCase   :Tuple						=			torch.mean(torch.abs(UpperCAmelCase   )   )

											assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3   ) < 1e-2
											assert abs(result_mean.item() - 0.3_3_6_2_0_3_8   ) < 1e-3
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Dict:
											pass
					def        UpperCAmelCase__ (							self							:	int   )	->       List[str]:
											pass







"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
		from PIL import Image




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Optional[Any]      =  """Salesforce/blip-image-captioning-base"""
					lowercase_    :				Optional[int]      =  (
					    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
					    """image to caption, and returns a text that contains the description in English."""
					)
					lowercase_    :				int      =  """image_captioner"""
					lowercase_    :				int      =  AutoModelForVisionaSeq

					lowercase_    :				List[str]      =  ["""image"""]
					lowercase_    :				int      =  ["""text"""]
					def __init__(							self							:	Any     , *UpperCAmelCase							:	str     , **UpperCAmelCase							:	List[str]   )	->       Union[str, Any]:
											requires_backends(self     , ['vision']   )
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	"Image"   )	->       Union[str, Any]:
											return self.pre_processor(images=UpperCAmelCase     , return_tensors='pt'   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	Optional[int]   )	->       Optional[int]:
											return self.model.generate(**UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	int   )	->       Dict:
											return self.pre_processor.batch_decode(UpperCAmelCase     , skip_special_tokens=UpperCAmelCase   )[0].strip()